Factors Influencing the Spatial Variability of Air Temperature Urban Heat Island Intensity in Chinese Cities

Few studies have investigated the spatial patterns of the air temperature urban heat island(AUHI)and its controlling factors.In this study,the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers.A total of 355 urban clusters were used.We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation.The results show that the midday AUHI is negatively correlated with climate wetness(humid:0.34 K;semi-humid:0.50 K;semi-arid:0.73 K).The annual mean midnight AUHI does not show discernible spatial patterns,but is generally stronger than the midday AUHI.The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid(0.32±0.09 K)and the semi-arid(0.36±0.11 K)climate zones.The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones.The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations.A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

A Preliminary Analysis of the Relationship between Longan Canopy Temperature and Air Temperature during Overwintering Period

[Objective] The study aimed to provide supports for developing chilling and freezing injury monitoring and disaster damage assessment of longan(Dimocarpus Longan Lour.).[Method] Based on field observation data,the relationships between longan canopy temperature and air temperature under different weather types(sunny,cloudy to sunny,cloudy,rainy,radiation chilling injury and advection chilling injury)in 2007-2008 winter were analyzed.[Result] Diurnal variations of longan canopy temperature under sunny and radiation chilling injury weather conditions were most dramatic,followed with those under cloudy to sunny condition,while variations under cloudy,rainy and advection chilling injury conditions were mild.Diurnal variations of orchard air temperature were also closely related to weather types.By using linear and curvilinear regression methods,the relationship models between longan canopy temperature and observation station air temperature were established.The models for cloudy,rainy and advection chilling injury had better effects than those for sunny,cloudy to sunny and radiation chilling injury;the models for night were better than those for daytime and the whole day.[Conclusion] To some extent,applying the relationship models between longan canopy temperature and observation station air temperature could make up the shortcoming of meteorological data which were higher than the real values.

Plant Temperature and Its Simulation Model of Thermo-Sensitive Genic Male Sterile Rice

Plant temperature （Tp） and its relations to the microclimate of rice colony and irrigation water were studied using a thermo-sensitive genic male sterile （TGMS） rice line, Pei＇ai 64S. Significant differences in the daily change of temperature were detected between Tp and air temperature at the height of 150 cm （TA）. From 8：00 to 20：00, Tp was lower than TA, but they were similar during 21：00 to next 7：00. The maximum Tp occurred one hour earlier than the maximum TA, though they both reached the minimum at 6：00. Tp fluctuated less than TA. At the same height, during 6：00-13：00, Tp was higher than air temperature （Ta）, and Tp reached the maximum one hour earlier than Ta. During the rest time on sunny day, Tp was close to or even a little lower than Ta. On overcast day, Tp was higher than Ta in the whole day, and both maximized at the same time. In addition, Tp was regulated by solar radiation, cloudage and wind speed in daytime, and by irrigation water at night. The present study indicated that a TA of 29.6℃ was the critical point, at which Tp was increased or decreased by irrigation water. Tp and the difference between water and air temperatures showed a conic relation. Tp fluctuation was also regulated by the absorption or reflection of solar radiation by leaves during daytime and release of heat energy during nighttime. By analysis on correlation and regression simulation, two models of Tp were established.

Effects of Photosynthetically Active Radiation and Air Temperature on CO_2 Uptake of Pterocarpus macrocarpus in the Open Field

Since trees and plants can absorb CO2, forests are widely regarded as a carbon sink that may control the amount of CO2 in the atmosphere. The CO2 uptake rate of plants is affected by the plant species and environmental conditions such as photosynthetically active radiation (PAR), temperature, water and nutrient contents. PAR is the most immediate environmental control on photosynthesis while air temperature affects both photorespiration and dark respiration. In the natural condition, PAR and temperature play an important role in net CO2 uptake. The effects of PAR and air temperature on the CO2 uptake of Pterocarpus macrocarpus grown in a natural habitat were studied in the present work. Due to many uncontrollable factors, a simple rectangular hyperbola could not represent the measured data. The data were divided into groups of 2oC intervals; CO2 uptake in each group may then be related to PAR by a rectangular hyperbola function. Using the obtained functions, the effect of PAR was removed from the original data. The PAR-independent CO2 uptake was then related to air temperature. Finally, the effects of PAR (I) and air temperature (Ta) on the CO2 uptake rate (A) were combined as: (-0.0575Ta2+2.6691Ta-23.264)I A= ——————————————— (-0.00766Ta2+0.40666Ta-3.99924) (-4.8794Ta2+227.13Ta-2456.9)+I

Simulation of the Effect of an Increase in Methane on Air Temperature

The infrared radiative effect of methane was analyzed using the 2D, interactive chemical dynamical radiative SOCRATES model of the National Center for Atmospheric Research. Then, a sensitivity experi ment, with the methane volume mixing ratio increased by 10%, was carried out to study the influence of an increase of methane on air temperature. The results showed that methane has a heating effect through the infrared radiative process in the troposphere and a cooling effect in the stratosphere. However, the cooling effect of the methane is much smaller than that of water vapor in the stratosphere and is negligible in the mesosphere. The simulation results also showed that when methane concentration is increased by 10%, the air temperature lowers in the stratosphere and mesosphere and increases in the troposphere. The cooling can reach 0.2 K at the stratopause and can vary from 0.2-0.4 K in the mesosphere, and the temperature rise varies by around 0.001-0.002 K in the troposphere. The cooling results from the increase of the infrared radiative cooling rate caused by increased water vapor and O3 concentration, which are stimulated by the increase in methane in most of the stratosphere. The infrared radiation cooling of methane itself is minor. The depletion of O3 stimulated by the methane increase results indirectly in a decrease in the rate of so- lar radiation heating, producing cooling in the stratopause and mesosphere. The tropospheric warming is mainly caused by the increase of methane, which produces infrared radiative heating. The increase in H2O and O3 caused by the methane increase also contributes to a rise in temperature in the troposphere.

Modeling of Air Temperature for Heat Exchange due to Vertical Turbulence and Horizontal Air Flow

In order to calculate the air temperature of the near surface layer in urban environment,the surface layer air was divided into several layers in the vertical direction,and some energy balance equations were developed for each air layer,in which the heat exchange due to vertical turbulence and horizontal air flow was taken into account.Then,the vertical temperature distribution of the surface layer air was obtained through the coupled calculation using the energy balance equations of underlying surfaces and building walls.Moreover,the measured air temperatures in a small area(with a horizontal scale of less than 500 m)and a large area(with a horizontal scale of more than 1 000 m)in Guangzhou in summer were used to validate the proposed model.The calculated results agree well with the measured ones,with a maximum relative error of 4.18%.It is thus concluded that the proposed model is a high-accuracy method to theoretically analyze the urban heat island and the thermal environment.

Near ground air temperature calculation model based on heat transfer of vertical turbulent and horizontal air flow

In order to calculate the air temperature of the near surface layer in urban environment,the surface layer air was divided into several sections in the vertical direction,and some energy balance equations were developed for each air layer,in which the heat exchange due to vertical turbulence and horizontal air flow was taken into account.Then,the vertical temperature distribution of the surface layer air was obtained through the coupled calculation using the energy balance equations of underlying surfaces and building walls.Moreover,the measured air temperatures in a small area(with a horizontal scale of less than 500 m) and a large area(with a horizontal scale of more than 1 000 m) in Guangzhou in summer were used to validate the proposed model.The calculated results accord well with the measured ones,with a maximum relative error of 4.18%.It is thus concluded that the proposed model is a high-accuracy method to theoretically analyze the urban heat island and the thermal environment.

On the long-term memory characteristic in land surface air temperatures:How well do CMIP6 models perform?

利用去趋势涨落分析(DFA)方法计算序列的长程记忆性(LTM),以CRUTEM5数据集的结果作为观测参照,评估了60个参与第六次国际耦合模式比较计划(CMIP6)的气候模式对地表气温LTM的再现能力.结果表明:大部分模式可以再现全球平均地表气温序列的LTM特征,其中AWI-ESM-1-1-LR和E3SM-1-0的模拟效果最好;60个模式均能模拟LTM随纬度带的变化;综合来说,全球水平上CNRM-CM6-1和HadGEM3-GC31-LL对地表气温LTM的模拟性能最好;多模式平均相比单一模式模拟性能更好;多模式平均与观测结果的偏差以及模式之间的模拟差异显著体现在赤道和沿海区域,这种偏差可能源于模式对海气耦合过程的模拟差异.

Interaction between Soil Moisture and Air Temperature in the Mississippi River Basin

Increasing air temperatures are expected to continue in the future. The relation between soil moisture and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relations between soil moisture and temperature was performed by developing a quantile regression model, a wavelet coherency model, and a Mann-Kendall correlation model from 1950 to 2010 in the Mississippi River Basin. The results indicate that first, anomaly air temperature is negatively correlated to anomaly soil moisture in the upper and lower basin, and however, the correlation between them are mixed in the middle basin. The correlation is stronger at the higher quantile (90th) of the two variables. Second, anomaly soil moisture and air temperature show strong coherency in annual frequency, indicating that the two variables are interannually correlated. Third, annual air temperature is significant negatively related to soil moisture, indicating that dry (wet) soil leads to warm (cool) weather in the basin. These results have potential application to future climate change research and water resource management. Also, the strong relationship between soil moisture and air temperature at annual scale could result in improved temperature predictability.

Variable Daily Air Temperature Model for Analysis and Design

An analytical model, TA(t), for the observed outside air temperature change, Ta(t), with time is developed using two components: one for the variation caused by the Earth’s movement, plus any other quasi-stationary thermodynamic effects due to industrialization;and one for the random variation caused by stochastic and/or chaotic, local environmental changes. The first component, TR(t), describes a regular trend, expressed by periodic functions of time and constants unchanged with time. The second component, TS, is a random, stochastic variation. For the observed outside air temperature, the analytical model of TA(t)=TR(t) +TS is such as to give a statistically best approximation for the observed time period with = min. Several versions for the TR(t) functions are defined and tested in the study for an example location for 20 years. The best model for TR(t) t is found as a linear function with time plus a variable-coefficient Fourier series with linearly changing amplitude with time. It is found that the final analytical temperature, TA(t), can be used not only to represent the historical daily mean temperature but also to predict the future daily mean temperature at the given location. The upper and lower boundaries give safety limits for the temperature prediction. The stochastic component identified in the model is stable and stationary. The method of model identification for TA(t) can be used for determining input temperature functions for supporting engineering design;or for an unbiased scientific inquiry of temperature change with time in climate studies.

Dynamical Downscaling of the Twentieth Century Reanalysis for China:Climatic Means during 1981–2010

This study presents a dynamically downscaled climatology over East Asia using the non-hydrostatic Weather Research and Forecasting （WRF） model, forced by the Twentieth Century Reanalysis （20CR-v2）. The whole experiment is a 111-year （1900--2010） continuous run at 50 km horizontal resolution. Comparisons of climatic means and seasonal cycles among observations, 20CR-v2, and WRF results during the last 30 years （1981-2010） in China are presented, with a focus on sur- face air temperature and precipitation in both summer and winter. The WRF results reproduce the main features of surface air temperature in the two seasons in China, and outperform 20CR-v2 in regional details due to topog- raphic forcing. Summer surface air temperature biases are reduced by as much as 1℃-2℃. For precipitation, the simulation results reproduce the decreasing pattern from Southeast to Northwest China in winter. For summer rainfall, the WRF simulation results reproduce the correct magnitude and position of heavy rainfall around the southeastern coastal area, and are better than 20CR-v2. One of the significant improvements is that an unrealistic center of summer precipitation in Southeast China present in 20CR-v2 is eliminated. However, the simulated results underestimate winter surface air temperature in northern China and winter rainfall in some regions in southeastern China. The mean seasonal cycles of surface air tempera- ture and precipitation are captured well over most of sub-regions by the WRF model.

Absorptive root-multidimension strategy links air temperature and species distribution in a montane forest

Background: Air temperature affects absorptive root traits, which are closely related to species distribution.However, it is still unclear how air temperature regulates species distribution through changes in absorptive root traits. Seven functional traits of the absorptive roots of 240 individuals of 52 species, soil properties and air temperature were measured along an elevational gradient on Mt. Fanjingshan, Tongren City, Guizhou, and then the direct and indirect effects of these controls on species distribution were detected.Results: Absorptive roots adapted to air temperature with two strategies. The first strategy was positively associated with the specific root area(SRA) and specific root length(SRL) and was negatively associated with the root tissue density(RTD), representing the classic root economics spectrum(RES). The second strategy was represented by the trade-off between root diameter, mycorrhizal fungi colonization(MF) and SRL, representing the collaboration gradient with “do it yourself” resource uptake ranging from “outsourcing” to mycorrhizal resource uptake. Air temperature regulated species distribution in six ways: directly reducing species importance value;indirectly increasing the species importance value by reducing soil nitrogen content or increasing soil pH by reducing soil moisture inducing absorptive roots to change from “do it yourself” resource absorption to “outsourcing” resource absorption;indirectly decreasing the species importance value by decreasing soil moisture to change from“outsourcing”resource absorption to “do it yourself” resource absorption;indirectly increasing the species importance value with increasing soil pH by reducing soil moisture resulting in absorptive root traits turning into nutrient foraging traits;and indirectly decreasing the species importance value by promoting absorptive root traits to nutrient conservation traits.Conclusions: Absorptive root traits play a crucial role in the regulation of species distribution through multiapproaches of air temperature.

具有时滞效应的air2stream河流水温模型及应用研究

高精度河流水温模型对于深入了解水温的时空变化特征和河流生态修复具有重要意义。基于数据驱动的air2stream模型在保证预测精度的同时,避免了计算的复杂性,已成为河流水温模拟常用的模型。由于水的热惯性及水文条件等的影响,河流水温变化往往显著滞后于气温变化,而air2stream原模型并未考虑滞后效应,导致该模型在流量未知情况下实际精度偏低。为解决该问题,采用气温-水温皮尔逊相关系数计算时滞天数,构建具有时滞的air2stream新模型,进一步根据长江中下游地区两个监测站的多年实测数据验证新模型的有效性和稳定性。结果表明:新模型在不引进额外观测数据的条件下具有更高精度且性能更稳定。相比原模型,在两个监测站新模型的均方根误差分别降低约4.29%和5.85%。新模型具有精度高、水文要素需求少的特点,可为长江中下游的水环境影响评价和生态保护提供依据。

Distributed modeling of monthly air temperatures over the rugged terrain of the Yellow River Basin

Our analyses of the monthly mean air temperature of meteorological stations show that altitude, global solar radiation and surface effective radiation have a significant impact on air temperature. We set up a physically-based empirical model for monthly air temperature simulation. Combined the proposed model with the distributed modeling results of global solar radiation and routine meteorological observation data, we also developed a method for the distributed simulation of monthly air temperatures over rugged terrain. Spatial distribution maps are generated at a resolution of 1 km×1 km for the monthly mean, the monthly mean maximum and the monthly mean minimum air temperatures for the Yellow River Basin. Analysis shows that the simulation results reflect to a considerable extent the macro and local distribution characteristics of air temperature. Cross-validation shows that the proposed model displays good stability with mean absolute bias errors of 0.19°C–0.35°C. Tests carried out on local meteorological station data and case year data show that the model has good spatial and temporal simulation capacity. The proposed model solely uses routine meteorological data and can be applied easily to other regions.

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models （GCMs） is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model （WRF） is a nextgeneration, fully compressible, Euler non-hydrostatic mesoscale forecast model with a runtime hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/ 1 h air temperatures by daily observed data of China Meteorological Administration （CMA） stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error （MBE） for each station is less than 2℃; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2℃, the R2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

Development and test verification of air temperature model for Chinese solar and Spainish Almeria-type greenhouses

Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological processes.Many of these processes have their limits genetically determined,but climate and irrigation play an important role.Because of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior climate.The main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into account.The final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse geometry.The model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating systems.The soil and the back wall are the main media for energy storage.The temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse system.The influences of different subsystems included in the temperature model were analyzed and evaluated.The results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,respectively.Thus,the model can be considered as a powerful tool for control design purposes in microclimate systems.

Detection and Projections of Climate Change in Rio de Janeiro, Brazil

A study on the detection and future projection of climate change in the city of Rio de Janeiro is here presented, based on the analysis of indices of temperature and precipitation extremes. The aim of this study is to provide information on observed and projected extremes in support of studies on impacts and vulnerability assessments required for adaptation strategies to climate change. Observational data from INMET’s weather stations and projections from INPE’s Eta- HadCM3 regional model are used. The observational analyses indicate that rainfall amount associated with heavy rain events is increasing in recent years in the forest region of Rio de Janeiro. An increase in both the frequency of occurrence and in the rainfall amount associated with heavy precipitation are projected until the end of the 21st Century, as are longer dry periods and shorter wet seasons. In regards to temperature, a warming trend is noted (both in past observations and future projections), with higher maximum air temperature and extremes. The average change in annual maximum (minimum) air temperatures may range between 2℃and 5℃(2℃and 4℃) above the current weather values in the late 21st Century. The warm (cold) days and nights are becoming more (less) frequent each year, and for the future climate (2100) it has been projected that about 40% to 70% of the days and 55% to 85% of the nights will be hot. Additionally, it can be foreseen that there will be no longer cold days and nights.

EIGEN-TEMPERATURE MODEL FOR THE ANNUAL AIR TEMPERATURE MOVEMENT EVALUATION AND FORECAST

Climate variability and its changes are issues of broader global concern.This study addresses the annual air temperature movement evaluation and forecasting based on principal component analysis(PCA).An Eigen-temperature model for describing the annual air temperature movement by employing PCA is introduced.Subspace for evaluation is generated by selecting principal orthogonal eigenvectors of covariance matrix of temperature data.The principal eigenvectors are called“Eigen-temperatures”,since they are eigenvectors and each temperature movement is described by them.Each temperature movement is projected onto the subspace of eigenspace,and described by a linear combination of the Eigen-temperatures.Then,a forecast method for the temperature movement by employing the Eigen-temperatures is proposed.Forecast is implemented with polynomial curve fitting algorithm to estimate subsequent representation weights for the subsequent temperature movement with respect to the“Eigen-temperatures”generated by its previous temperature movements.The proposed Eigen-temperature model is applied to evaluation and forecasting for annual temperature movement at Tongchuan observation station of China from 1962 to 1971 and from 1994 to 2002.Experimental results agreeing well with actual observation values show workability of the proposed.Result analysis indicates its effectiveness that the proposed Eigen-temperature model is outperforming the classical AR model and the BP-ANN on the forecast tasks.